• Jamilu Awwalu Nigerian Defence Academy https://orcid.org/0000-0002-3607-3274
  • Nana Aisha Umar Department of Information Technology, Faculty of Computing, Federal University Dutse.
  • Mubaraka Sani Ibrahim Department of Computer Science, Faculty of Computing, Baze University Abuja.
  • Ogwueleka Francisca Nonyelum Department of Computer Science, Faculty of Military Science and Interdisciplinary Studies, Nigerian Defence Academy.
Keywords: COVID-19, Multinomial Naïve Bayes, Detection, Decision Support System,Website


Coronavirus disease 2019 termed COVID-19 is a highly infectious and pathogenic illness caused by severe acute respiratory syndrome. Symptoms of COVID-19 range from mild to severe, in some cases leading to death. Early detection could help to monitor progression of the disease, mitigate spread of the disease and possibly reduce mortality rate. Computer-aided diagnosis systems are designed to complement health care systems and assist in the early detection of diseases. Currently, as it is not possible to test all citizens especially in developing countries with very large populations due to financial constraints and the standard of their healthcare facilities, the problem of identifying suspected cases and deciding laboratory test priority among citizens is evident and more pressing. Therefore in this study, we introduce an interactive Artificial Intelligent web system using the Multinomial Naïve Bayes algorithm with the aim of detecting warning COVID-19 symptoms and to provide fitting suggestions.  Furthermore, the study also evaluates the performance of the Multinomial Naïve Bayes based on the different holdout approaches experimented. The experimental results are promising as the Multinomial Naïve Bayes is shown to achieve high accuracy detection thus providing a reliable method to identify warning symptoms of COVID-19.


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How to Cite
AwwaluJ., UmarN. A., IbrahimM. S., & NonyelumO. F. (2020). A MULTINOMIAL NAÏVE BAYES DECISION SUPPORT SYSTEM FOR COVID-19 DETECTION. FUDMA JOURNAL OF SCIENCES, 4(2), 704 - 711. https://doi.org/10.33003/fjs-2020-0402-331

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